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How OpenAI Builds for 800 Million Weekly Active Users: Model Specialization and Fine-Tuning We sat down with Sherwin Wu, Head of Engineering at OpenAI Platform, to discuss OpenAI’s developer strategy, how to manage top ML teams, why they decided to start releasing open-weight models again, how prompt engineering has...

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113,950 次观看 • 7 个月前 •via X (Twitter)

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In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

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BREAKING: Inside Thrive Capital w/ Partner Philip Clark Investing in OpenAI, Wiz, Cursor, Nudge, Physical Intelligence “Josh always had a line to me when I joined Thrive, which is that the people who win deals are the ones who want to win them most.” In this conversation, Philip breaks down how one of the most concentrated & influential firms (Thrive Capital) in tech evaluates founders, builds conviction, & partners with companies that reshape the world. We dive into: - Seeing an early demo of OpenAI’s GPT-4 before launch - Why Thrive flew into an active war zone to close the Wiz deal - Cursor’s explosive growth from a small pivot to a multi-hundred-million ARR product - How Nudge is engineering the human brain using ultrasound - Why hardware’s barriers are falling & why the biggest companies of the next decade may be physical Highlights (00:00) Who is Philip Clark? How he joined Thrive Capital (02:45) From physics to investing: becoming a technologist–optimist (04:00) How semiconductors led him to Thrive (06:00) Deep dive: Mesh Optical & the data center interconnect opportunity (07:45) Inside Cursor’s explosive growth and why AI is “speed chess” (09:15) How Philip first met Cursor’s founders during a pivot (11:30) Path to partner & Thrive’s “full-stack investor” model (13:45) The Wiz story: flying into an active war zone (17:15) Why Wiz closed six-figure deals in weeks — the rare “fast + big” enterprise combo (19:30) The rise of hardware: sensors, software, & SpaceX-trained talent (21:45) The rise of Nudge and engineering the human brain (26:30) Neuralink & Nudge: read to stimulate (31:15) Why Thrive concentrates instead of “spray and pray” (36:00) Inside OpenAI: seeing GPT-4 before launch (40:45) What comes after SaaS.. & the companies unlocked by AI
57:43

BREAKING: Inside Thrive Capital w/ Partner Philip Clark Investing in OpenAI, Wiz, Cursor, Nudge, Physical Intelligence “Josh always had a line to me when I joined Thrive, which is that the people who win deals are the ones who want to win them most.” In this conversation, Philip breaks down how one of the most concentrated & influential firms (Thrive Capital) in tech evaluates founders, builds conviction, & partners with companies that reshape the world. We dive into: - Seeing an early demo of OpenAI’s GPT-4 before launch - Why Thrive flew into an active war zone to close the Wiz deal - Cursor’s explosive growth from a small pivot to a multi-hundred-million ARR product - How Nudge is engineering the human brain using ultrasound - Why hardware’s barriers are falling & why the biggest companies of the next decade may be physical Highlights (00:00) Who is Philip Clark? How he joined Thrive Capital (02:45) From physics to investing: becoming a technologist–optimist (04:00) How semiconductors led him to Thrive (06:00) Deep dive: Mesh Optical & the data center interconnect opportunity (07:45) Inside Cursor’s explosive growth and why AI is “speed chess” (09:15) How Philip first met Cursor’s founders during a pivot (11:30) Path to partner & Thrive’s “full-stack investor” model (13:45) The Wiz story: flying into an active war zone (17:15) Why Wiz closed six-figure deals in weeks — the rare “fast + big” enterprise combo (19:30) The rise of hardware: sensors, software, & SpaceX-trained talent (21:45) The rise of Nudge and engineering the human brain (26:30) Neuralink & Nudge: read to stimulate (31:15) Why Thrive concentrates instead of “spray and pray” (36:00) Inside OpenAI: seeing GPT-4 before launch (40:45) What comes after SaaS.. & the companies unlocked by AI

Molly O’Shea

362,011 次观看 • 7 个月前

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science
49:06

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science

Matt Turck

64,952 次观看 • 1 个月前

OpenAI’s hottest app isn’t ChatGPT—it’s Codex. In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl. I talked to Thibault (Tibo), head of Codex, and Andrew (Andrew Ambrosino), a member of technical staff who built the Codex app, for Every 📧’s AI & I about what OpenAI is building and how they’re using it internally. We get into: - Why they built a GUI instead of a terminal. Terminals work for quick tasks, they say, but feel limiting when you’re running multiple agents in parallel. The IDE, meanwhile, overwhelms users—and the Codex team wants the AI to dynamically decide which tools to show you for a given task. - How they’re teaching the model to read between the lines. Codex is great at following instructions, but optimize too hard in that direction, and it starts taking you literally—like copying a typo directly into the code. The team obsesses over this tradeoff, and is also introducing “personalities,” modes users can toggle between that control how blunt or supportive the model feels. - How OpenAI uses its own coding agent. Codex lets you schedule prompts to run on a recurring basis, and the team has dozens of automations running at all times. For example, one scans for merge conflicts every couple of hours so code is always ready to ship, and another picks a random file from the codebase multiple times a day and hunts for bugs no one would've gone looking for. - Why speed is a dimension of intelligence. OpenAI’s newest model (Spark) is so fast that they actually slow it down so you can read the output. They see the speed enabling three things: staying super in the flow, replacing brittle developer tools with intelligent ones that can adapt on the fly, and redirecting the model mid-task— especially with voice—so coding starts to feel more and more like a conversation. - Code review is the next bottleneck. Models can generate code faster than ever, but someone still has to verify that it works. The team is exploring a future where the model proves its own fix works—retracing the click path a user would take, screenshotting the results, and attaching the evidence to a pull request. This is a must-watch for anyone who uses AI coding agents—and is curious about the future of programming. Watch below! Timestamps: Introduction: 00:01:27 OpenAI’s evolving bet on its coding agent: 00:05:27 The choice to invest in a GUI (over a terminal): 00:09:42 The AI workflows that the Codex team relies on to ship: 00:20:38 Teaching Codex how to read between the lines: 00:26:45 Building affordances for a lightening fast model: 00:28:45 Why speed is a dimension of intelligence: 00:33:15 Code review is the next bottleneck for coding agents: 00:36:30 How the Codex team positions against the competition: 00:41:24
46:40

OpenAI’s hottest app isn’t ChatGPT—it’s Codex. In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl. I talked to Thibault (Tibo), head of Codex, and Andrew (Andrew Ambrosino), a member of technical staff who built the Codex app, for Every 📧’s AI & I about what OpenAI is building and how they’re using it internally. We get into: - Why they built a GUI instead of a terminal. Terminals work for quick tasks, they say, but feel limiting when you’re running multiple agents in parallel. The IDE, meanwhile, overwhelms users—and the Codex team wants the AI to dynamically decide which tools to show you for a given task. - How they’re teaching the model to read between the lines. Codex is great at following instructions, but optimize too hard in that direction, and it starts taking you literally—like copying a typo directly into the code. The team obsesses over this tradeoff, and is also introducing “personalities,” modes users can toggle between that control how blunt or supportive the model feels. - How OpenAI uses its own coding agent. Codex lets you schedule prompts to run on a recurring basis, and the team has dozens of automations running at all times. For example, one scans for merge conflicts every couple of hours so code is always ready to ship, and another picks a random file from the codebase multiple times a day and hunts for bugs no one would've gone looking for. - Why speed is a dimension of intelligence. OpenAI’s newest model (Spark) is so fast that they actually slow it down so you can read the output. They see the speed enabling three things: staying super in the flow, replacing brittle developer tools with intelligent ones that can adapt on the fly, and redirecting the model mid-task— especially with voice—so coding starts to feel more and more like a conversation. - Code review is the next bottleneck. Models can generate code faster than ever, but someone still has to verify that it works. The team is exploring a future where the model proves its own fix works—retracing the click path a user would take, screenshotting the results, and attaching the evidence to a pull request. This is a must-watch for anyone who uses AI coding agents—and is curious about the future of programming. Watch below! Timestamps: Introduction: 00:01:27 OpenAI’s evolving bet on its coding agent: 00:05:27 The choice to invest in a GUI (over a terminal): 00:09:42 The AI workflows that the Codex team relies on to ship: 00:20:38 Teaching Codex how to read between the lines: 00:26:45 Building affordances for a lightening fast model: 00:28:45 Why speed is a dimension of intelligence: 00:33:15 Code review is the next bottleneck for coding agents: 00:36:30 How the Codex team positions against the competition: 00:41:24

Dan Shipper 📧

15,588 次观看 • 4 个月前

"I ran a startup for 5 years. I thought we moved fast. Then I joined OpenAI—and realized I had no idea what fast actually meant." Head of Product for OpenAI's Codex Alexander Embiricos and I chat about: 🔸 How OpenAI built the Sora Android app (which hit #1) in just 18 days by using Codex 🔸 Codex growing 20x since August 🔸 How they're thinking about the dev bottleneck shifting from coding to reviewing AI-generated work 🔸 Why coding will be a core competency for every AI agent—because writing code is how agents use computers best 🔸 Why the bottleneck to AGI-level productivity isn’t model capability—it’s human typing speed 🔸 The vision of Codex, and how they plan to win Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 Fin — The #1 AI agent for customer service: 🏆 Atlassian Jira Product Discovery — Confidence to build the right thing:
1:38

"I ran a startup for 5 years. I thought we moved fast. Then I joined OpenAI—and realized I had no idea what fast actually meant." Head of Product for OpenAI's Codex Alexander Embiricos and I chat about: 🔸 How OpenAI built the Sora Android app (which hit #1) in just 18 days by using Codex 🔸 Codex growing 20x since August 🔸 How they're thinking about the dev bottleneck shifting from coding to reviewing AI-generated work 🔸 Why coding will be a core competency for every AI agent—because writing code is how agents use computers best 🔸 Why the bottleneck to AGI-level productivity isn’t model capability—it’s human typing speed 🔸 The vision of Codex, and how they plan to win Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 Fin — The #1 AI agent for customer service: 🏆 Atlassian Jira Product Discovery — Confidence to build the right thing:

Lenny Rachitsky

334,422 次观看 • 7 个月前

Inside Block: How AI Changes Software Development Block's Owen Jennings sat down with a16z GP David Haber to discuss how AI is changing software businesses, including the end of handwritten code, why Block reduced its workforce by 40%, how small teams are doing more with agents, and more. 00:00 Introduction 09:08 The most meaningful difference in how Block is operating 12:57 AI infrastructure build across the org 17:09 The shape of the business: Square, Cash App, Afterpay 20:00 From static UI to generative UI 23:23 Defensibility in the AI era OBJ David Haber Block
27:16

Inside Block: How AI Changes Software Development Block's Owen Jennings sat down with a16z GP David Haber to discuss how AI is changing software businesses, including the end of handwritten code, why Block reduced its workforce by 40%, how small teams are doing more with agents, and more. 00:00 Introduction 09:08 The most meaningful difference in how Block is operating 12:57 AI infrastructure build across the org 17:09 The shape of the business: Square, Cash App, Afterpay 20:00 From static UI to generative UI 23:23 Defensibility in the AI era OBJ David Haber Block

a16z

93,210 次观看 • 3 个月前

AI Winners, Token Economics, and Venture Strategy w/ David Clark and David George VenCap CIO David Clark joins a16z GP David George to discuss the AI supercycle and how it's changing the technology industry, including what bigger exits mean for venture capital, why we're not in a bubble, why we're still early in enterprise AI adoption, and more. 00:00 Intro 00:38 The scale shift: Anthropic & OpenAI adding more revenue than hyperscalers 04:20 Skeuomorphic vs native AI applications in the enterprise 06:24 How the best AI companies run themselves differently 08:14 Top 1% exits 10x'd in 24 months 11:17 The half-life problem: Why 40% of AI leaders drop off every year 13:11 Token economics, cost pressure, and who captures value 17:00 Loss ratios, risk, and how to think about early stage 22:51 Are we in an AI bubble? 29:50 The future of venture capital in an AI world David Clark David George
33:09

AI Winners, Token Economics, and Venture Strategy w/ David Clark and David George VenCap CIO David Clark joins a16z GP David George to discuss the AI supercycle and how it's changing the technology industry, including what bigger exits mean for venture capital, why we're not in a bubble, why we're still early in enterprise AI adoption, and more. 00:00 Intro 00:38 The scale shift: Anthropic & OpenAI adding more revenue than hyperscalers 04:20 Skeuomorphic vs native AI applications in the enterprise 06:24 How the best AI companies run themselves differently 08:14 Top 1% exits 10x'd in 24 months 11:17 The half-life problem: Why 40% of AI leaders drop off every year 13:11 Token economics, cost pressure, and who captures value 17:00 Loss ratios, risk, and how to think about early stage 22:51 Are we in an AI bubble? 29:50 The future of venture capital in an AI world David Clark David George

a16z

42,120 次观看 • 1 个月前

I sat down with Jordi Visser to discuss the SpaceX IPO, orbital data centers, and the critical minerals powering the AI buildout. We also discuss the AI model wars, why Jordi thinks Sam Altman won't be running OpenAI within a year, and how the New York Knicks playoff run connects to the future of crypto and blockchain in a world of AI and deep fakes. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 1:00 - SpaceX IPO, Elon & orbital data centers 12:15 - Critical minerals & the AI supply chain 18:55 - American industrial sovereignty & critical chemicals 22:36 - AI model cost crisis & who wins the model war 37:10 - Knicks NBA Finals & the case for crypto in an AI world 47:48 - Jeff Bezos launches Prometheus 50:34 - AI use case of the week
54:47

I sat down with Jordi Visser to discuss the SpaceX IPO, orbital data centers, and the critical minerals powering the AI buildout. We also discuss the AI model wars, why Jordi thinks Sam Altman won't be running OpenAI within a year, and how the New York Knicks playoff run connects to the future of crypto and blockchain in a world of AI and deep fakes. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 1:00 - SpaceX IPO, Elon & orbital data centers 12:15 - Critical minerals & the AI supply chain 18:55 - American industrial sovereignty & critical chemicals 22:36 - AI model cost crisis & who wins the model war 37:10 - Knicks NBA Finals & the case for crypto in an AI world 47:48 - Jeff Bezos launches Prometheus 50:34 - AI use case of the week

Anthony Pompliano 🌪

133,301 次观看 • 1 个月前